Module 2- Applications in GIS- Forestry and LiDAR

    This week we worked with Virginia LiDAR data from Shenandoah National Park to create a DEM and DSM, forest height and forest biomass (canopy density) layers. I found the LiDAR data a bit tricky. The layers themselves flowed smoothly but, when viewing in 2D, I never could get my LiDAR data to populate on my map without zooming in so far that it rendered the visual pointless. Regardless, I was able to work around this by creating a 3D map with the LiDAR layer, the 3D model had less trouble populating than the 2D although I still couldn't view it zoomed out as much as I needed to for my final map.

We used the Point File Information tool to summarize the contents of the LiDAR layer and then created a DEM layer using the LAS Dataset to Raster tool with ground points. We created a DSM layer using Non-Ground Points. For the heigh layer we subtracted the DEM from the DSM using the minus tool.

To calculate biomass density, we used the LAS to multipoint tool and created a layer representing the ground and a layer representing the vegetation. We converted these layers to rasters with a “count” assignment. We used the IS NULL tool to create a binary file for each layer, then use the Con tool on these created layers to accept as true if value of 0 is encountered and to pull a value of 1 from the original raster to pull the ground and vegetation layers separately.

We used the Plus tool to combine these layers and the Float tool to transform the information from an integer to a float. Finally, we calculated density using the Divide tool to compare the vegetation raster that was created using the con tool with the float data. I realized ¾ of the way through this process that this would have been a great time to have used the Model Builder we learned about in GIS Programming last term, but since I had already created so many files it had become counterproductive to use it at this point. If I was doing these calculations frequently, I would create a model to use to make the process more efficient and leave less room for error. Eventually I had so many features I was working with, and I was trying to remember which one was from which step even with intentional naming patterns. A model would have been very useful.

Finally, we created a histogram chart to display the height data created earlier in the lab. The exercise had said negative values were in error, but I was having a hard time finding how to exclude the handful of negative data points. For our vector data we excluded the point in the symbology pane, but this wouldn’t work for the raster data set. Eventually I discovered a button called “Selection” which I could toggle on and off that, when combined with a SQL clause, allowed me to exclude data as needed. I used this to remove all data points below 0 from my histogram.

Here are 3 maps that I created from some of the many layers I created in this process.

Ideally I would have zoomed out a bit more for this lidar image but I this was the farthest I could zoom out without it switching to only showing the extent lines.